REVA-QCAV/train.py

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import sys
import os
from optparse import OptionParser
import numpy as np
import torch
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import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
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from eval import eval_net
from unet import UNet
from utils import get_ids, split_ids, split_train_val, get_imgs_and_masks, batch
def train_net(net,
epochs=5,
batch_size=1,
lr=0.1,
val_percent=0.05,
save_cp=True,
gpu=False,
img_scale=0.5):
dir_img = 'data/train/'
dir_mask = 'data/train_masks/'
dir_checkpoint = 'checkpoints/'
ids = get_ids(dir_img)
ids = split_ids(ids)
iddataset = split_train_val(ids, val_percent)
print('''
Starting training:
Epochs: {}
Batch size: {}
Learning rate: {}
Training size: {}
Validation size: {}
Checkpoints: {}
CUDA: {}
'''.format(epochs, batch_size, lr, len(iddataset['train']),
len(iddataset['val']), str(save_cp), str(gpu)))
N_train = len(iddataset['train'])
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optimizer = optim.SGD(net.parameters(),
lr=lr,
momentum=0.9,
weight_decay=0.0005)
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criterion = nn.BCELoss()
for epoch in range(epochs):
print('Starting epoch {}/{}.'.format(epoch + 1, epochs))
net.train()
# reset the generators
train = get_imgs_and_masks(iddataset['train'], dir_img, dir_mask, img_scale)
val = get_imgs_and_masks(iddataset['val'], dir_img, dir_mask, img_scale)
epoch_loss = 0
for i, b in enumerate(batch(train, batch_size)):
imgs = np.array([i[0] for i in b]).astype(np.float32)
true_masks = np.array([i[1] for i in b])
imgs = torch.from_numpy(imgs)
true_masks = torch.from_numpy(true_masks)
if gpu:
imgs = imgs.cuda()
true_masks = true_masks.cuda()
masks_pred = net(imgs)
masks_probs = F.sigmoid(masks_pred)
masks_probs_flat = masks_probs.view(-1)
true_masks_flat = true_masks.view(-1)
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loss = criterion(masks_probs_flat, true_masks_flat)
epoch_loss += loss.item()
print('{0:.4f} --- loss: {1:.6f}'.format(i * batch_size / N_train, loss.item()))
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optimizer.zero_grad()
loss.backward()
optimizer.step()
print('Epoch finished ! Loss: {}'.format(epoch_loss / i))
if 1:
val_dice = eval_net(net, val, gpu)
print('Validation Dice Coeff: {}'.format(val_dice))
if save_cp:
torch.save(net.state_dict(),
dir_checkpoint + 'CP{}.pth'.format(epoch + 1))
print('Checkpoint {} saved !'.format(epoch + 1))
def get_args():
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parser = OptionParser()
parser.add_option('-e', '--epochs', dest='epochs', default=5, type='int',
help='number of epochs')
parser.add_option('-b', '--batch-size', dest='batchsize', default=10,
type='int', help='batch size')
parser.add_option('-l', '--learning-rate', dest='lr', default=0.1,
type='float', help='learning rate')
parser.add_option('-g', '--gpu', action='store_true', dest='gpu',
default=False, help='use cuda')
parser.add_option('-c', '--load', dest='load',
default=False, help='load file model')
parser.add_option('-s', '--scale', dest='scale', type='float',
default=0.5, help='downscaling factor of the images')
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(options, args) = parser.parse_args()
return options
if __name__ == '__main__':
args = get_args()
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net = UNet(n_channels=3, n_classes=1)
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if args.load:
net.load_state_dict(torch.load(args.load))
print('Model loaded from {}'.format(args.load))
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if args.gpu:
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net.cuda()
# cudnn.benchmark = True # faster convolutions, but more memory
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try:
train_net(net=net,
epochs=args.epochs,
batch_size=args.batchsize,
lr=args.lr,
gpu=args.gpu,
img_scale=args.scale)
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except KeyboardInterrupt:
torch.save(net.state_dict(), 'INTERRUPTED.pth')
print('Saved interrupt')
try:
sys.exit(0)
except SystemExit:
os._exit(0)